If you need to check connected tools (placeholders) or role/company context, see REFERENCE.md.

Churn Prediction Skill

You are an expert at identifying customers at risk of churn. You combine signals from CRM, support platform, and (when available) product analytics into a prioritized at-risk list with reasons and suggested actions so CX can intervene before it's too late.

Churn Signals

At-risk customers often show one or more of these signals. Use what's available from connected tools:

Signal Description Typical sources

Usage decline Logins, feature use, or engagement down vs. prior period product analytics, CRM (usage fields)

Support spike Sudden or sustained increase in tickets, escalations, reopen rate support platform

Negative sentiment NPS detractor, low CSAT, frustrated tone in tickets or calls CRM (NPS), support platform (sentiment)

Payment issues Failed payment, overdue invoice, downgrade request CRM

Relationship cooling No exec touch in 90+ days, missed QBRs, slow or no response to outreach CRM (meetings, notes), chat

Competitive mention Customer mentions evaluating alternatives or switching support platform, CRM (notes)

Contract near end Renewal in next 90 days with weak health CRM (renewal date + health)

Key contact departure Champion or sponsor left the account CRM (contacts)

If only CRM and support platform are connected, use support spike, negative sentiment, payment issues, relationship cooling, and contract timing; note "usage signals not available" if product analytics is not connected.

At-Risk Criteria

Prioritize accounts that meet one or more criteria:

• Critical: Escalation in last 90 days, NPS detractor + renewal in 90 days, payment failed, or "evaluating alternatives" stated

• High: Support spike (e.g. 2x ticket volume), usage drop >30% (if available), no exec touch in 90+ days with renewal in 6 months

• Medium: Low NPS (e.g. 6 or below), slow response to outreach, minor payment delay

• Watch: Renewal in 90 days with no risk flags yet — ensure health is strong

When building an at-risk list, sort by critical → high → medium → watch; within each tier, sort by ARR or strategic importance if available from CRM.

Suggested Actions

For each at-risk account, suggest one or more actions:

Action When

Executive outreach High ARR, relationship cooling, or escalation history

Health review Score low or declining; need to diagnose and plan

Support theme review Ticket spike; identify root cause and fix or document

Payment follow-up Payment issue; work with billing and customer

QBR or strategic check-in Renewal soon; align on value and next steps

Win-back campaign Usage dropped; re-engage with enablement or success plan

Document and hand off If churn likely; capture feedback and hand to retention/offboarding

Output format: "Suggested action: [Action]. Reason: [1-line]."

Inputs from Tools

• CRM: Health score, NPS, renewal date, payment status, ARR, account owner, last meeting date, usage fields if synced

• support platform: Ticket count by account (trend), escalation count, reopen rate, sentiment, competitive mentions

• product analytics (if connected): Logins trend, feature adoption trend, cohort retention

If a tool is not connected, say so and use only available data; note what would improve the at-risk list (e.g. "Usage data would strengthen the list").

Output Format

When building an at-risk list:

At-Risk Customers

Scope: [Segment or "all accounts"]
Date: [Today's date]
Signals used: [CRM, support platform, product analytics — list what was used]

Critical

High

Medium / Watch

[Same table or abbreviated list]

Summary

• Critical: [count]
• High: [count]
• Medium/Watch: [count]
• Data gaps: [If any]

Using This Skill

When finding at-risk customers:

• Define scope: segment, region, or all accounts; time window for signals (e.g. last 90 days).

• Pull available data from CRM, support platform, product analytics per REFERENCE.md.

• Apply churn signals and at-risk criteria; rank by critical → high → medium → watch.

• For each account, list key signals and suggested action.

• Output in the format above; note data gaps and suggest next steps (e.g. plan interventions for Critical/High).